[{"publication":"ACM Transactions on Modeling and Computer Simulation","status":"public","publisher":"ACM","department":[{"_id":"ToHe"},{"_id":"GaTk"}],"_id":"1861","article_processing_charge":"No","isi":1,"acknowledgement":"HYCON2; EC; European Commission\r\n","quality_controlled":"1","external_id":{"isi":["000354789200002"]},"issue":"2","date_created":"2018-12-11T11:54:25Z","day":"01","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","publication_status":"published","intvolume":"        25","publist_id":"5238","language":[{"iso":"eng"}],"date_published":"2015-02-01T00:00:00Z","volume":25,"doi":"10.1145/2688906","month":"02","scopus_import":"1","abstract":[{"text":"Continuous-time Markov chains are commonly used in practice for modeling biochemical reaction networks in which the inherent randomness of themolecular interactions cannot be ignored. This has motivated recent research effort into methods for parameter inference and experiment design for such models. The major difficulty is that such methods usually require one to iteratively solve the chemical master equation that governs the time evolution of the probability distribution of the system. This, however, is rarely possible, and even approximation techniques remain limited to relatively small and simple systems. An alternative explored in this article is to base methods on only some low-order moments of the entire probability distribution. We summarize the theory behind such moment-based methods for parameter inference and experiment design and provide new case studies where we investigate their performance.","lang":"eng"}],"article_number":"8","citation":{"ieee":"J. Ruess and J. Lygeros, “Moment-based methods for parameter inference and experiment design for stochastic biochemical reaction networks,” <i>ACM Transactions on Modeling and Computer Simulation</i>, vol. 25, no. 2. ACM, 2015.","mla":"Ruess, Jakob, and John Lygeros. “Moment-Based Methods for Parameter Inference and Experiment Design for Stochastic Biochemical Reaction Networks.” <i>ACM Transactions on Modeling and Computer Simulation</i>, vol. 25, no. 2, 8, ACM, 2015, doi:<a href=\"https://doi.org/10.1145/2688906\">10.1145/2688906</a>.","chicago":"Ruess, Jakob, and John Lygeros. “Moment-Based Methods for Parameter Inference and Experiment Design for Stochastic Biochemical Reaction Networks.” <i>ACM Transactions on Modeling and Computer Simulation</i>. ACM, 2015. <a href=\"https://doi.org/10.1145/2688906\">https://doi.org/10.1145/2688906</a>.","ista":"Ruess J, Lygeros J. 2015. Moment-based methods for parameter inference and experiment design for stochastic biochemical reaction networks. ACM Transactions on Modeling and Computer Simulation. 25(2), 8.","short":"J. Ruess, J. Lygeros, ACM Transactions on Modeling and Computer Simulation 25 (2015).","apa":"Ruess, J., &#38; Lygeros, J. (2015). Moment-based methods for parameter inference and experiment design for stochastic biochemical reaction networks. <i>ACM Transactions on Modeling and Computer Simulation</i>. ACM. <a href=\"https://doi.org/10.1145/2688906\">https://doi.org/10.1145/2688906</a>","ama":"Ruess J, Lygeros J. Moment-based methods for parameter inference and experiment design for stochastic biochemical reaction networks. <i>ACM Transactions on Modeling and Computer Simulation</i>. 2015;25(2). doi:<a href=\"https://doi.org/10.1145/2688906\">10.1145/2688906</a>"},"author":[{"id":"4A245D00-F248-11E8-B48F-1D18A9856A87","full_name":"Ruess, Jakob","last_name":"Ruess","first_name":"Jakob","orcid":"0000-0003-1615-3282"},{"first_name":"John","last_name":"Lygeros","full_name":"Lygeros, John"}],"oa_version":"None","year":"2015","title":"Moment-based methods for parameter inference and experiment design for stochastic biochemical reaction networks","type":"journal_article","date_updated":"2025-09-23T09:36:19Z"},{"date_published":"2015-01-01T00:00:00Z","volume":199,"corr_author":"1","language":[{"iso":"eng"}],"scopus_import":"1","doi":"10.1534/genetics.114.171850","month":"01","oa_version":"Preprint","author":[{"orcid":"0000-0002-6699-1455","first_name":"Gasper","last_name":"Tkacik","full_name":"Tkacik, Gasper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Julien","last_name":"Dubuis","full_name":"Dubuis, Julien"},{"full_name":"Petkova, Mariela","first_name":"Mariela","last_name":"Petkova"},{"first_name":"Thomas","last_name":"Gregor","full_name":"Gregor, Thomas"}],"abstract":[{"lang":"eng","text":"The concept of positional information is central to our understanding of how cells determine their location in a multicellular structure and thereby their developmental fates. Nevertheless, positional information has neither been defined mathematically nor quantified in a principled way. Here we provide an information-theoretic definition in the context of developmental gene expression patterns and examine the features of expression patterns that affect positional information quantitatively. We connect positional information with the concept of positional error and develop tools to directly measure information and error from experimental data. We illustrate our framework for the case of gap gene expression patterns in the early Drosophila embryo and show how information that is distributed among only four genes is sufficient to determine developmental fates with nearly single-cell resolution. Our approach can be generalized to a variety of different model systems; procedures and examples are discussed in detail. "}],"citation":{"short":"G. Tkačik, J. Dubuis, M. Petkova, T. Gregor, Genetics 199 (2015) 39–59.","ista":"Tkačik G, Dubuis J, Petkova M, Gregor T. 2015. Positional information, positional error, and readout precision in morphogenesis: A mathematical framework. Genetics. 199(1), 39–59.","chicago":"Tkačik, Gašper, Julien Dubuis, Mariela Petkova, and Thomas Gregor. “Positional Information, Positional Error, and Readout Precision in Morphogenesis: A Mathematical Framework.” <i>Genetics</i>. Genetics Society of America, 2015. <a href=\"https://doi.org/10.1534/genetics.114.171850\">https://doi.org/10.1534/genetics.114.171850</a>.","ieee":"G. Tkačik, J. Dubuis, M. Petkova, and T. Gregor, “Positional information, positional error, and readout precision in morphogenesis: A mathematical framework,” <i>Genetics</i>, vol. 199, no. 1. Genetics Society of America, pp. 39–59, 2015.","mla":"Tkačik, Gašper, et al. “Positional Information, Positional Error, and Readout Precision in Morphogenesis: A Mathematical Framework.” <i>Genetics</i>, vol. 199, no. 1, Genetics Society of America, 2015, pp. 39–59, doi:<a href=\"https://doi.org/10.1534/genetics.114.171850\">10.1534/genetics.114.171850</a>.","ama":"Tkačik G, Dubuis J, Petkova M, Gregor T. Positional information, positional error, and readout precision in morphogenesis: A mathematical framework. <i>Genetics</i>. 2015;199(1):39-59. doi:<a href=\"https://doi.org/10.1534/genetics.114.171850\">10.1534/genetics.114.171850</a>","apa":"Tkačik, G., Dubuis, J., Petkova, M., &#38; Gregor, T. (2015). Positional information, positional error, and readout precision in morphogenesis: A mathematical framework. <i>Genetics</i>. Genetics Society of America. <a href=\"https://doi.org/10.1534/genetics.114.171850\">https://doi.org/10.1534/genetics.114.171850</a>"},"date_updated":"2025-09-23T09:53:07Z","arxiv":1,"type":"journal_article","title":"Positional information, positional error, and readout precision in morphogenesis: A mathematical framework","year":"2015","oa":1,"publisher":"Genetics Society of America","department":[{"_id":"GaTk"}],"page":"39 - 59","publication":"Genetics","status":"public","external_id":{"isi":["000347712900004"],"arxiv":["1404.5599"]},"quality_controlled":"1","_id":"1885","article_processing_charge":"No","isi":1,"user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","main_file_link":[{"open_access":"1","url":"http://arxiv.org/abs/1404.5599"}],"day":"01","date_created":"2018-12-11T11:54:32Z","issue":"1","publist_id":"5210","intvolume":"       199","publication_status":"published"},{"publist_id":"5145","intvolume":"        91","publication_status":"published","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","main_file_link":[{"url":"http://arxiv.org/abs/1501.04015","open_access":"1"}],"date_created":"2018-12-11T11:54:49Z","day":"15","issue":"6","external_id":{"isi":["000356131600006"],"arxiv":["1501.04015"]},"quality_controlled":"1","_id":"1940","article_processing_charge":"No","isi":1,"oa":1,"publisher":"American Institute of Physics","department":[{"_id":"GaTk"}],"publication":"Physical Review E Statistical Nonlinear and Soft Matter Physics","status":"public","date_updated":"2025-09-23T09:46:10Z","type":"journal_article","arxiv":1,"title":"Optimizing information flow in small genetic networks. IV. Spatial coupling","year":"2015","oa_version":"Preprint","author":[{"orcid":"0000-0002-1287-3779","first_name":"Thomas R","last_name":"Sokolowski","full_name":"Sokolowski, Thomas R","id":"3E999752-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Tkacik","orcid":"0000-0002-6699-1455","first_name":"Gasper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","full_name":"Tkacik, Gasper"}],"abstract":[{"text":"We typically think of cells as responding to external signals independently by regulating their gene expression levels, yet they often locally exchange information and coordinate. Can such spatial coupling be of benefit for conveying signals subject to gene regulatory noise? Here we extend our information-theoretic framework for gene regulation to spatially extended systems. As an example, we consider a lattice of nuclei responding to a concentration field of a transcriptional regulator (the &quot;input&quot;) by expressing a single diffusible target gene. When input concentrations are low, diffusive coupling markedly improves information transmission; optimal gene activation functions also systematically change. A qualitatively new regulatory strategy emerges where individual cells respond to the input in a nearly step-like fashion that is subsequently averaged out by strong diffusion. While motivated by early patterning events in the Drosophila embryo, our framework is generically applicable to spatially coupled stochastic gene expression models.","lang":"eng"}],"article_number":"062710","citation":{"mla":"Sokolowski, Thomas R., and Gašper Tkačik. “Optimizing Information Flow in Small Genetic Networks. IV. Spatial Coupling.” <i>Physical Review E Statistical Nonlinear and Soft Matter Physics</i>, vol. 91, no. 6, 062710, American Institute of Physics, 2015, doi:<a href=\"https://doi.org/10.1103/PhysRevE.91.062710\">10.1103/PhysRevE.91.062710</a>.","ieee":"T. R. Sokolowski and G. Tkačik, “Optimizing information flow in small genetic networks. IV. Spatial coupling,” <i>Physical Review E Statistical Nonlinear and Soft Matter Physics</i>, vol. 91, no. 6. American Institute of Physics, 2015.","ista":"Sokolowski TR, Tkačik G. 2015. Optimizing information flow in small genetic networks. IV. Spatial coupling. Physical Review E Statistical Nonlinear and Soft Matter Physics. 91(6), 062710.","chicago":"Sokolowski, Thomas R, and Gašper Tkačik. “Optimizing Information Flow in Small Genetic Networks. IV. Spatial Coupling.” <i>Physical Review E Statistical Nonlinear and Soft Matter Physics</i>. American Institute of Physics, 2015. <a href=\"https://doi.org/10.1103/PhysRevE.91.062710\">https://doi.org/10.1103/PhysRevE.91.062710</a>.","short":"T.R. Sokolowski, G. Tkačik, Physical Review E Statistical Nonlinear and Soft Matter Physics 91 (2015).","apa":"Sokolowski, T. R., &#38; Tkačik, G. (2015). Optimizing information flow in small genetic networks. IV. Spatial coupling. <i>Physical Review E Statistical Nonlinear and Soft Matter Physics</i>. American Institute of Physics. <a href=\"https://doi.org/10.1103/PhysRevE.91.062710\">https://doi.org/10.1103/PhysRevE.91.062710</a>","ama":"Sokolowski TR, Tkačik G. Optimizing information flow in small genetic networks. IV. Spatial coupling. <i>Physical Review E Statistical Nonlinear and Soft Matter Physics</i>. 2015;91(6). doi:<a href=\"https://doi.org/10.1103/PhysRevE.91.062710\">10.1103/PhysRevE.91.062710</a>"},"scopus_import":"1","doi":"10.1103/PhysRevE.91.062710","month":"06","date_published":"2015-06-15T00:00:00Z","volume":91,"corr_author":"1","language":[{"iso":"eng"}]},{"date_published":"2015-06-10T00:00:00Z","volume":3,"ec_funded":1,"file_date_updated":"2022-02-25T11:55:26Z","project":[{"grant_number":"291734","_id":"25681D80-B435-11E9-9278-68D0E5697425","name":"International IST Postdoc Fellowship Programme","call_identifier":"FP7"}],"has_accepted_license":"1","corr_author":"1","language":[{"iso":"eng"}],"article_type":"original","scopus_import":"1","doi":"10.3389/fenvs.2015.00042","month":"06","oa_version":"Published Version","author":[{"last_name":"Parise","first_name":"Francesca","full_name":"Parise, Francesca"},{"full_name":"Lygeros, John","first_name":"John","last_name":"Lygeros"},{"first_name":"Jakob","orcid":"0000-0003-1615-3282","last_name":"Ruess","full_name":"Ruess, Jakob","id":"4A245D00-F248-11E8-B48F-1D18A9856A87"}],"abstract":[{"text":"Mathematical models are of fundamental importance in the understanding of complex population dynamics. For instance, they can be used to predict the population evolution starting from different initial conditions or to test how a system responds to external perturbations. For this analysis to be meaningful in real applications, however, it is of paramount importance to choose an appropriate model structure and to infer the model parameters from measured data. While many parameter inference methods are available for models based on deterministic ordinary differential equations, the same does not hold for more detailed individual-based models. Here we consider, in particular, stochastic models in which the time evolution of the species abundances is described by a continuous-time Markov chain. These models are governed by a master equation that is typically difficult to solve. Consequently, traditional inference methods that rely on iterative evaluation of parameter likelihoods are computationally intractable. The aim of this paper is to present recent advances in parameter inference for continuous-time Markov chain models, based on a moment closure approximation of the parameter likelihood, and to investigate how these results can help in understanding, and ultimately controlling, complex systems in ecology. Specifically, we illustrate through an agricultural pest case study how parameters of a stochastic individual-based model can be identified from measured data and how the resulting model can be used to solve an optimal control problem in a stochastic setting. In particular, we show how the matter of determining the optimal combination of two different pest control methods can be formulated as a chance constrained optimization problem where the control action is modeled as a state reset, leading to a hybrid system formulation.","lang":"eng"}],"citation":{"short":"F. Parise, J. Lygeros, J. Ruess, Frontiers in Environmental Science 3 (2015).","ieee":"F. Parise, J. Lygeros, and J. Ruess, “Bayesian inference for stochastic individual-based models of ecological systems: a pest control simulation study,” <i>Frontiers in Environmental Science</i>, vol. 3. Frontiers, 2015.","mla":"Parise, Francesca, et al. “Bayesian Inference for Stochastic Individual-Based Models of Ecological Systems: A Pest Control Simulation Study.” <i>Frontiers in Environmental Science</i>, vol. 3, 42, Frontiers, 2015, doi:<a href=\"https://doi.org/10.3389/fenvs.2015.00042\">10.3389/fenvs.2015.00042</a>.","chicago":"Parise, Francesca, John Lygeros, and Jakob Ruess. “Bayesian Inference for Stochastic Individual-Based Models of Ecological Systems: A Pest Control Simulation Study.” <i>Frontiers in Environmental Science</i>. Frontiers, 2015. <a href=\"https://doi.org/10.3389/fenvs.2015.00042\">https://doi.org/10.3389/fenvs.2015.00042</a>.","ista":"Parise F, Lygeros J, Ruess J. 2015. Bayesian inference for stochastic individual-based models of ecological systems: a pest control simulation study. Frontiers in Environmental Science. 3, 42.","ama":"Parise F, Lygeros J, Ruess J. Bayesian inference for stochastic individual-based models of ecological systems: a pest control simulation study. <i>Frontiers in Environmental Science</i>. 2015;3. doi:<a href=\"https://doi.org/10.3389/fenvs.2015.00042\">10.3389/fenvs.2015.00042</a>","apa":"Parise, F., Lygeros, J., &#38; Ruess, J. (2015). Bayesian inference for stochastic individual-based models of ecological systems: a pest control simulation study. <i>Frontiers in Environmental Science</i>. Frontiers. <a href=\"https://doi.org/10.3389/fenvs.2015.00042\">https://doi.org/10.3389/fenvs.2015.00042</a>"},"article_number":"42","date_updated":"2025-04-15T06:50:01Z","publication_identifier":{"issn":["2296-665X"]},"type":"journal_article","title":"Bayesian inference for stochastic individual-based models of ecological systems: a pest control simulation study","year":"2015","oa":1,"file":[{"success":1,"file_name":"2015_FrontiersEnvironmScience_Parise.pdf","file_size":1371201,"date_created":"2022-02-25T11:55:26Z","checksum":"26c222487564e1be02a11d688d6f769d","access_level":"open_access","content_type":"application/pdf","date_updated":"2022-02-25T11:55:26Z","creator":"dernst","relation":"main_file","file_id":"10795"}],"publisher":"Frontiers","department":[{"_id":"ToHe"},{"_id":"GaTk"}],"publication":"Frontiers in Environmental Science","status":"public","ddc":["000","570"],"quality_controlled":"1","acknowledgement":"The authors would like to acknowledge contributions from Baptiste Mottet who performed preliminary analysis regarding parameter inference for the considered case study in a student project (Mottet, 2014/2015).\r\nThe research leading to these results has received funding from the People Programme (Marie Curie Actions) of the European Union's Seventh Framework Programme (FP7/2007-2013) under REA grant agreement No. [291734] and from SystemsX under the project SignalX.","article_processing_charge":"No","_id":"10794","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_created":"2022-02-25T11:42:25Z","day":"10","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png"},"intvolume":"         3","keyword":["General Environmental Science"],"publication_status":"published"},{"date_published":"2015-11-06T00:00:00Z","department":[{"_id":"NiBa"},{"_id":"CaGu"},{"_id":"GaTk"}],"publisher":"Public Library of Science","status":"public","_id":"9712","doi":"10.1371/journal.pgen.1005639.s001","month":"11","article_processing_charge":"No","oa_version":"Published Version","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","author":[{"id":"37C323C6-F248-11E8-B48F-1D18A9856A87","full_name":"Tugrul, Murat","last_name":"Tugrul","first_name":"Murat","orcid":"0000-0002-8523-0758"},{"last_name":"Paixao","first_name":"Tiago","orcid":"0000-0003-2361-3953","id":"2C5658E6-F248-11E8-B48F-1D18A9856A87","full_name":"Paixao, Tiago"},{"id":"4880FE40-F248-11E8-B48F-1D18A9856A87","full_name":"Barton, Nicholas H","last_name":"Barton","orcid":"0000-0002-8548-5240","first_name":"Nicholas H"},{"orcid":"0000-0002-6699-1455","first_name":"Gašper","last_name":"Tkačik","full_name":"Tkačik, Gašper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87"}],"date_created":"2021-07-23T12:00:37Z","day":"06","citation":{"mla":"Tugrul, Murat, et al. <i>Other Fitness Models for Comparison &#38; for Interacting TFBSs</i>. Public Library of Science, 2015, doi:<a href=\"https://doi.org/10.1371/journal.pgen.1005639.s001\">10.1371/journal.pgen.1005639.s001</a>.","ieee":"M. Tugrul, T. Paixao, N. H. Barton, and G. Tkačik, “Other fitness models for comparison &#38; for interacting TFBSs.” Public Library of Science, 2015.","ista":"Tugrul M, Paixao T, Barton NH, Tkačik G. 2015. Other fitness models for comparison &#38; for interacting TFBSs, Public Library of Science, <a href=\"https://doi.org/10.1371/journal.pgen.1005639.s001\">10.1371/journal.pgen.1005639.s001</a>.","chicago":"Tugrul, Murat, Tiago Paixao, Nicholas H Barton, and Gašper Tkačik. “Other Fitness Models for Comparison &#38; for Interacting TFBSs.” Public Library of Science, 2015. <a href=\"https://doi.org/10.1371/journal.pgen.1005639.s001\">https://doi.org/10.1371/journal.pgen.1005639.s001</a>.","short":"M. Tugrul, T. Paixao, N.H. Barton, G. Tkačik, (2015).","apa":"Tugrul, M., Paixao, T., Barton, N. H., &#38; Tkačik, G. (2015). Other fitness models for comparison &#38; for interacting TFBSs. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pgen.1005639.s001\">https://doi.org/10.1371/journal.pgen.1005639.s001</a>","ama":"Tugrul M, Paixao T, Barton NH, Tkačik G. Other fitness models for comparison &#38; for interacting TFBSs. 2015. doi:<a href=\"https://doi.org/10.1371/journal.pgen.1005639.s001\">10.1371/journal.pgen.1005639.s001</a>"},"date_updated":"2025-09-23T08:31:14Z","type":"research_data_reference","title":"Other fitness models for comparison & for interacting TFBSs","related_material":{"record":[{"status":"public","relation":"used_in_publication","id":"1666"}]},"year":"2015"},{"doi":"10.1371/journal.pcbi.1004055.s001","_id":"9718","article_processing_charge":"No","month":"03","date_published":"2015-03-23T00:00:00Z","department":[{"_id":"GaTk"}],"publisher":"Public Library of Science","status":"public","date_updated":"2025-09-23T08:43:16Z","type":"research_data_reference","title":"Supporting information text","related_material":{"record":[{"id":"1827","relation":"used_in_publication","status":"public"}]},"year":"2015","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","oa_version":"Published Version","author":[{"last_name":"Friedlander","first_name":"Tamar","id":"36A5845C-F248-11E8-B48F-1D18A9856A87","full_name":"Friedlander, Tamar"},{"full_name":"Mayo, Avraham E.","first_name":"Avraham E.","last_name":"Mayo"},{"full_name":"Tlusty, Tsvi","first_name":"Tsvi","last_name":"Tlusty"},{"full_name":"Alon, Uri","first_name":"Uri","last_name":"Alon"}],"day":"23","date_created":"2021-07-26T08:35:23Z","citation":{"chicago":"Friedlander, Tamar, Avraham E. Mayo, Tsvi Tlusty, and Uri Alon. “Supporting Information Text.” Public Library of Science, 2015. <a href=\"https://doi.org/10.1371/journal.pcbi.1004055.s001\">https://doi.org/10.1371/journal.pcbi.1004055.s001</a>.","ista":"Friedlander T, Mayo AE, Tlusty T, Alon U. 2015. Supporting information text, Public Library of Science, <a href=\"https://doi.org/10.1371/journal.pcbi.1004055.s001\">10.1371/journal.pcbi.1004055.s001</a>.","mla":"Friedlander, Tamar, et al. <i>Supporting Information Text</i>. Public Library of Science, 2015, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1004055.s001\">10.1371/journal.pcbi.1004055.s001</a>.","ieee":"T. Friedlander, A. E. Mayo, T. Tlusty, and U. Alon, “Supporting information text.” Public Library of Science, 2015.","short":"T. Friedlander, A.E. Mayo, T. Tlusty, U. Alon, (2015).","apa":"Friedlander, T., Mayo, A. E., Tlusty, T., &#38; Alon, U. (2015). Supporting information text. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1004055.s001\">https://doi.org/10.1371/journal.pcbi.1004055.s001</a>","ama":"Friedlander T, Mayo AE, Tlusty T, Alon U. Supporting information text. 2015. doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1004055.s001\">10.1371/journal.pcbi.1004055.s001</a>"}},{"type":"research_data_reference","date_updated":"2025-09-23T08:43:16Z","year":"2015","title":"Evolutionary simulation code","related_material":{"record":[{"id":"1827","status":"public","relation":"used_in_publication"}]},"author":[{"full_name":"Friedlander, Tamar","id":"36A5845C-F248-11E8-B48F-1D18A9856A87","first_name":"Tamar","last_name":"Friedlander"},{"full_name":"Mayo, Avraham E.","first_name":"Avraham E.","last_name":"Mayo"},{"full_name":"Tlusty, Tsvi","last_name":"Tlusty","first_name":"Tsvi"},{"first_name":"Uri","last_name":"Alon","full_name":"Alon, Uri"}],"oa_version":"Published Version","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","citation":{"short":"T. Friedlander, A.E. Mayo, T. Tlusty, U. Alon, (2015).","ista":"Friedlander T, Mayo AE, Tlusty T, Alon U. 2015. Evolutionary simulation code, Public Library of Science, <a href=\"https://doi.org/10.1371/journal.pcbi.1004055.s002\">10.1371/journal.pcbi.1004055.s002</a>.","chicago":"Friedlander, Tamar, Avraham E. Mayo, Tsvi Tlusty, and Uri Alon. “Evolutionary Simulation Code.” Public Library of Science, 2015. <a href=\"https://doi.org/10.1371/journal.pcbi.1004055.s002\">https://doi.org/10.1371/journal.pcbi.1004055.s002</a>.","ieee":"T. Friedlander, A. E. Mayo, T. Tlusty, and U. Alon, “Evolutionary simulation code.” Public Library of Science, 2015.","mla":"Friedlander, Tamar, et al. <i>Evolutionary Simulation Code</i>. Public Library of Science, 2015, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1004055.s002\">10.1371/journal.pcbi.1004055.s002</a>.","ama":"Friedlander T, Mayo AE, Tlusty T, Alon U. Evolutionary simulation code. 2015. doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1004055.s002\">10.1371/journal.pcbi.1004055.s002</a>","apa":"Friedlander, T., Mayo, A. E., Tlusty, T., &#38; Alon, U. (2015). Evolutionary simulation code. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1004055.s002\">https://doi.org/10.1371/journal.pcbi.1004055.s002</a>"},"day":"23","date_created":"2021-08-05T12:58:07Z","month":"03","_id":"9773","doi":"10.1371/journal.pcbi.1004055.s002","article_processing_charge":"No","department":[{"_id":"GaTk"}],"publisher":"Public Library of Science","date_published":"2015-03-23T00:00:00Z","status":"public"},{"ddc":["576"],"external_id":{"isi":["000352195700006"]},"quality_controlled":"1","article_processing_charge":"No","_id":"1827","isi":1,"publisher":"Public Library of Science","department":[{"_id":"GaTk"}],"file":[{"file_size":1811647,"date_created":"2018-12-12T10:15:39Z","checksum":"b8aa66f450ff8de393014b87ec7d2efb","file_name":"IST-2016-452-v1+1_journal.pcbi.1004055.pdf","content_type":"application/pdf","date_updated":"2020-07-14T12:45:17Z","access_level":"open_access","creator":"system","relation":"main_file","file_id":"5161"}],"oa":1,"publication":"PLoS Computational Biology","status":"public","publist_id":"5278","publication_status":"published","intvolume":"        11","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","issue":"3","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png"},"date_created":"2018-12-11T11:54:14Z","day":"23","scopus_import":"1","doi":"10.1371/journal.pcbi.1004055","month":"03","pubrep_id":"452","has_accepted_license":"1","file_date_updated":"2020-07-14T12:45:17Z","project":[{"_id":"25681D80-B435-11E9-9278-68D0E5697425","grant_number":"291734","name":"International IST Postdoc Fellowship Programme","call_identifier":"FP7"}],"volume":11,"date_published":"2015-03-23T00:00:00Z","ec_funded":1,"language":[{"iso":"eng"}],"type":"journal_article","date_updated":"2025-09-23T08:43:16Z","year":"2015","related_material":{"record":[{"relation":"research_data","status":"public","id":"9718"},{"status":"public","relation":"research_data","id":"9773"}]},"title":"Evolution of bow-tie architectures in biology","author":[{"full_name":"Friedlander, Tamar","id":"36A5845C-F248-11E8-B48F-1D18A9856A87","first_name":"Tamar","last_name":"Friedlander"},{"full_name":"Mayo, Avraham","first_name":"Avraham","last_name":"Mayo"},{"last_name":"Tlusty","first_name":"Tsvi","full_name":"Tlusty, Tsvi"},{"first_name":"Uri","last_name":"Alon","full_name":"Alon, Uri"}],"oa_version":"Published Version","abstract":[{"text":"Bow-tie or hourglass structure is a common architectural feature found in many biological systems. A bow-tie in a multi-layered structure occurs when intermediate layers have much fewer components than the input and output layers. Examples include metabolism where a handful of building blocks mediate between multiple input nutrients and multiple output biomass components, and signaling networks where information from numerous receptor types passes through a small set of signaling pathways to regulate multiple output genes. Little is known, however, about how bow-tie architectures evolve. Here, we address the evolution of bow-tie architectures using simulations of multi-layered systems evolving to fulfill a given input-output goal. We find that bow-ties spontaneously evolve when the information in the evolutionary goal can be compressed. Mathematically speaking, bow-ties evolve when the rank of the input-output matrix describing the evolutionary goal is deficient. The maximal compression possible (the rank of the goal) determines the size of the narrowest part of the network—that is the bow-tie. A further requirement is that a process is active to reduce the number of links in the network, such as product-rule mutations, otherwise a non-bow-tie solution is found in the evolutionary simulations. This offers a mechanism to understand a common architectural principle of biological systems, and a way to quantitate the effective rank of the goals under which they evolved.","lang":"eng"}],"citation":{"apa":"Friedlander, T., Mayo, A., Tlusty, T., &#38; Alon, U. (2015). Evolution of bow-tie architectures in biology. <i>PLoS Computational Biology</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1004055\">https://doi.org/10.1371/journal.pcbi.1004055</a>","ama":"Friedlander T, Mayo A, Tlusty T, Alon U. Evolution of bow-tie architectures in biology. <i>PLoS Computational Biology</i>. 2015;11(3). doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1004055\">10.1371/journal.pcbi.1004055</a>","chicago":"Friedlander, Tamar, Avraham Mayo, Tsvi Tlusty, and Uri Alon. “Evolution of Bow-Tie Architectures in Biology.” <i>PLoS Computational Biology</i>. Public Library of Science, 2015. <a href=\"https://doi.org/10.1371/journal.pcbi.1004055\">https://doi.org/10.1371/journal.pcbi.1004055</a>.","ista":"Friedlander T, Mayo A, Tlusty T, Alon U. 2015. Evolution of bow-tie architectures in biology. PLoS Computational Biology. 11(3).","ieee":"T. Friedlander, A. Mayo, T. Tlusty, and U. Alon, “Evolution of bow-tie architectures in biology,” <i>PLoS Computational Biology</i>, vol. 11, no. 3. Public Library of Science, 2015.","mla":"Friedlander, Tamar, et al. “Evolution of Bow-Tie Architectures in Biology.” <i>PLoS Computational Biology</i>, vol. 11, no. 3, Public Library of Science, 2015, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1004055\">10.1371/journal.pcbi.1004055</a>.","short":"T. Friedlander, A. Mayo, T. Tlusty, U. Alon, PLoS Computational Biology 11 (2015)."}},{"publication":"PLoS Genetics","status":"public","publisher":"Public Library of Science","department":[{"_id":"NiBa"},{"_id":"CaGu"},{"_id":"GaTk"}],"file":[{"access_level":"open_access","date_updated":"2020-07-14T12:45:10Z","content_type":"application/pdf","relation":"main_file","file_id":"4657","creator":"system","file_name":"IST-2016-463-v1+1_journal.pgen.1005639.pdf","checksum":"a4e72fca5ccf40ddacf4d08c8e46b554","date_created":"2018-12-12T10:07:58Z","file_size":2580778}],"oa":1,"_id":"1666","article_processing_charge":"No","isi":1,"ddc":["576"],"quality_controlled":"1","external_id":{"isi":["000366179000022"]},"issue":"11","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png"},"day":"06","date_created":"2018-12-11T11:53:21Z","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","publication_status":"published","intvolume":"        11","publist_id":"5483","language":[{"iso":"eng"}],"project":[{"call_identifier":"FP7","name":"Limits to selection in biology and in evolutionary computation","grant_number":"250152","_id":"25B07788-B435-11E9-9278-68D0E5697425"}],"file_date_updated":"2020-07-14T12:45:10Z","has_accepted_license":"1","date_published":"2015-11-06T00:00:00Z","volume":11,"ec_funded":1,"month":"11","pubrep_id":"463","doi":"10.1371/journal.pgen.1005639","scopus_import":"1","abstract":[{"text":"Evolution of gene regulation is crucial for our understanding of the phenotypic differences between species, populations and individuals. Sequence-specific binding of transcription factors to the regulatory regions on the DNA is a key regulatory mechanism that determines gene expression and hence heritable phenotypic variation. We use a biophysical model for directional selection on gene expression to estimate the rates of gain and loss of transcription factor binding sites (TFBS) in finite populations under both point and insertion/deletion mutations. Our results show that these rates are typically slow for a single TFBS in an isolated DNA region, unless the selection is extremely strong. These rates decrease drastically with increasing TFBS length or increasingly specific protein-DNA interactions, making the evolution of sites longer than ∼ 10 bp unlikely on typical eukaryotic speciation timescales. Similarly, evolution converges to the stationary distribution of binding sequences very slowly, making the equilibrium assumption questionable. The availability of longer regulatory sequences in which multiple binding sites can evolve simultaneously, the presence of “pre-sites” or partially decayed old sites in the initial sequence, and biophysical cooperativity between transcription factors, can all facilitate gain of TFBS and reconcile theoretical calculations with timescales inferred from comparative genomics.","lang":"eng"}],"citation":{"ama":"Tugrul M, Paixao T, Barton NH, Tkačik G. Dynamics of transcription factor binding site evolution. <i>PLoS Genetics</i>. 2015;11(11). doi:<a href=\"https://doi.org/10.1371/journal.pgen.1005639\">10.1371/journal.pgen.1005639</a>","apa":"Tugrul, M., Paixao, T., Barton, N. H., &#38; Tkačik, G. (2015). Dynamics of transcription factor binding site evolution. <i>PLoS Genetics</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pgen.1005639\">https://doi.org/10.1371/journal.pgen.1005639</a>","short":"M. Tugrul, T. Paixao, N.H. Barton, G. Tkačik, PLoS Genetics 11 (2015).","ieee":"M. Tugrul, T. Paixao, N. H. Barton, and G. Tkačik, “Dynamics of transcription factor binding site evolution,” <i>PLoS Genetics</i>, vol. 11, no. 11. Public Library of Science, 2015.","mla":"Tugrul, Murat, et al. “Dynamics of Transcription Factor Binding Site Evolution.” <i>PLoS Genetics</i>, vol. 11, no. 11, Public Library of Science, 2015, doi:<a href=\"https://doi.org/10.1371/journal.pgen.1005639\">10.1371/journal.pgen.1005639</a>.","ista":"Tugrul M, Paixao T, Barton NH, Tkačik G. 2015. Dynamics of transcription factor binding site evolution. PLoS Genetics. 11(11).","chicago":"Tugrul, Murat, Tiago Paixao, Nicholas H Barton, and Gašper Tkačik. “Dynamics of Transcription Factor Binding Site Evolution.” <i>PLoS Genetics</i>. Public Library of Science, 2015. <a href=\"https://doi.org/10.1371/journal.pgen.1005639\">https://doi.org/10.1371/journal.pgen.1005639</a>."},"author":[{"orcid":"0000-0002-8523-0758","first_name":"Murat","last_name":"Tugrul","full_name":"Tugrul, Murat","id":"37C323C6-F248-11E8-B48F-1D18A9856A87"},{"id":"2C5658E6-F248-11E8-B48F-1D18A9856A87","full_name":"Paixao, Tiago","last_name":"Paixao","orcid":"0000-0003-2361-3953","first_name":"Tiago"},{"full_name":"Barton, Nicholas H","id":"4880FE40-F248-11E8-B48F-1D18A9856A87","first_name":"Nicholas H","orcid":"0000-0002-8548-5240","last_name":"Barton"},{"first_name":"Gasper","orcid":"0000-0002-6699-1455","last_name":"Tkacik","full_name":"Tkacik, Gasper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87"}],"oa_version":"Published Version","year":"2015","related_material":{"record":[{"relation":"research_data","status":"public","id":"9712"},{"id":"1131","status":"public","relation":"dissertation_contains"}]},"title":"Dynamics of transcription factor binding site evolution","type":"journal_article","date_updated":"2026-07-29T11:31:13Z"},{"publisher":"Biophysical Society","department":[{"_id":"GaTk"}],"page":"1194 - 1204","oa":1,"publication":"Biophysical Journal","status":"public","quality_controlled":"1","external_id":{"pmid":["24606943"],"isi":["000332501300022"]},"_id":"2231","article_processing_charge":"No","isi":1,"main_file_link":[{"open_access":"1","url":"http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4026790/"}],"user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","issue":"5","pmid":1,"date_created":"2018-12-11T11:56:28Z","day":"04","publist_id":"4730","publication_status":"published","intvolume":"       106","corr_author":"1","volume":106,"date_published":"2014-03-04T00:00:00Z","language":[{"iso":"eng"}],"scopus_import":"1","doi":"10.1016/j.bpj.2014.01.014","month":"03","author":[{"last_name":"Rieckh","first_name":"Georg","id":"34DA8BD6-F248-11E8-B48F-1D18A9856A87","full_name":"Rieckh, Georg"},{"orcid":"0000-0002-6699-1455","first_name":"Gasper","last_name":"Tkacik","full_name":"Tkacik, Gasper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87"}],"oa_version":"Submitted Version","abstract":[{"text":"Based on the measurements of noise in gene expression performed during the past decade, it has become customary to think of gene regulation in terms of a two-state model, where the promoter of a gene can stochastically switch between an ON and an OFF state. As experiments are becoming increasingly precise and the deviations from the two-state model start to be observable, we ask about the experimental signatures of complex multistate promoters, as well as the functional consequences of this additional complexity. In detail, we i), extend the calculations for noise in gene expression to promoters described by state transition diagrams with multiple states, ii), systematically compute the experimentally accessible noise characteristics for these complex promoters, and iii), use information theory to evaluate the channel capacities of complex promoter architectures and compare them with the baseline provided by the two-state model. We find that adding internal states to the promoter generically decreases channel capacity, except in certain cases, three of which (cooperativity, dual-role regulation, promoter cycling) we analyze in detail.","lang":"eng"}],"citation":{"ama":"Rieckh G, Tkačik G. Noise and information transmission in promoters with multiple internal states. <i>Biophysical Journal</i>. 2014;106(5):1194-1204. doi:<a href=\"https://doi.org/10.1016/j.bpj.2014.01.014\">10.1016/j.bpj.2014.01.014</a>","apa":"Rieckh, G., &#38; Tkačik, G. (2014). Noise and information transmission in promoters with multiple internal states. <i>Biophysical Journal</i>. Biophysical Society. <a href=\"https://doi.org/10.1016/j.bpj.2014.01.014\">https://doi.org/10.1016/j.bpj.2014.01.014</a>","short":"G. Rieckh, G. Tkačik, Biophysical Journal 106 (2014) 1194–1204.","ieee":"G. Rieckh and G. Tkačik, “Noise and information transmission in promoters with multiple internal states,” <i>Biophysical Journal</i>, vol. 106, no. 5. Biophysical Society, pp. 1194–1204, 2014.","mla":"Rieckh, Georg, and Gašper Tkačik. “Noise and Information Transmission in Promoters with Multiple Internal States.” <i>Biophysical Journal</i>, vol. 106, no. 5, Biophysical Society, 2014, pp. 1194–204, doi:<a href=\"https://doi.org/10.1016/j.bpj.2014.01.014\">10.1016/j.bpj.2014.01.014</a>.","chicago":"Rieckh, Georg, and Gašper Tkačik. “Noise and Information Transmission in Promoters with Multiple Internal States.” <i>Biophysical Journal</i>. Biophysical Society, 2014. <a href=\"https://doi.org/10.1016/j.bpj.2014.01.014\">https://doi.org/10.1016/j.bpj.2014.01.014</a>.","ista":"Rieckh G, Tkačik G. 2014. Noise and information transmission in promoters with multiple internal states. Biophysical Journal. 106(5), 1194–1204."},"publication_identifier":{"issn":["0006-3495"]},"type":"journal_article","date_updated":"2025-09-29T11:23:30Z","year":"2014","title":"Noise and information transmission in promoters with multiple internal states"},{"publist_id":"4689","publication_status":"published","intvolume":"        10","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png"},"issue":"1","date_created":"2018-12-11T11:56:36Z","day":"02","ddc":["570"],"external_id":{"isi":["000337948500010"]},"quality_controlled":"1","article_processing_charge":"No","_id":"2257","isi":1,"acknowledgement":"This work was funded by NSF grant IIS-0613435, NSF grant PHY-0957573, NSF grant CCF-0939370, NIH grant R01 EY14196, NIH grant P50 GM071508, the Fannie and John Hertz Foundation, the Swartz Foundation, the WM Keck Foundation, ANR Optima and the French State program “Investissements d'Avenir” [LIFESENSES: ANR-10-LABX-65], and the Austrian Research Foundation FWF P25651.","publisher":"Public Library of Science","department":[{"_id":"GaTk"}],"oa":1,"file":[{"checksum":"c720222c5e924a4acb17f23b9381a6ca","file_size":2194790,"date_created":"2018-12-12T10:12:46Z","file_name":"IST-2016-436-v1+1_journal.pcbi.1003408.pdf","date_updated":"2020-07-14T12:45:35Z","content_type":"application/pdf","access_level":"open_access","file_id":"4965","relation":"main_file","creator":"system"}],"publication":"PLoS Computational Biology","status":"public","publication_identifier":{"issn":["1553-734X"]},"type":"journal_article","date_updated":"2025-09-29T11:14:06Z","year":"2014","related_material":{"record":[{"status":"public","relation":"research_data","id":"5562"}]},"title":"Searching for collective behavior in a large network of sensory neurons","author":[{"full_name":"Tkacik, Gasper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gasper","orcid":"0000-0002-6699-1455","last_name":"Tkacik"},{"full_name":"Marre, Olivier","last_name":"Marre","first_name":"Olivier"},{"last_name":"Amodei","first_name":"Dario","full_name":"Amodei, Dario"},{"last_name":"Schneidman","first_name":"Elad","full_name":"Schneidman, Elad"},{"last_name":"Bialek","first_name":"William","full_name":"Bialek, William"},{"full_name":"Berry, Michael","last_name":"Berry","first_name":"Michael"}],"oa_version":"Published Version","abstract":[{"text":"Maximum entropy models are the least structured probability distributions that exactly reproduce a chosen set of statistics measured in an interacting network. Here we use this principle to construct probabilistic models which describe the correlated spiking activity of populations of up to 120 neurons in the salamander retina as it responds to natural movies. Already in groups as small as 10 neurons, interactions between spikes can no longer be regarded as small perturbations in an otherwise independent system; for 40 or more neurons pairwise interactions need to be supplemented by a global interaction that controls the distribution of synchrony in the population. Here we show that such “K-pairwise” models—being systematic extensions of the previously used pairwise Ising models—provide an excellent account of the data. We explore the properties of the neural vocabulary by: 1) estimating its entropy, which constrains the population's capacity to represent visual information; 2) classifying activity patterns into a small set of metastable collective modes; 3) showing that the neural codeword ensembles are extremely inhomogenous; 4) demonstrating that the state of individual neurons is highly predictable from the rest of the population, allowing the capacity for error correction.","lang":"eng"}],"citation":{"ama":"Tkačik G, Marre O, Amodei D, Schneidman E, Bialek W, Berry M. Searching for collective behavior in a large network of sensory neurons. <i>PLoS Computational Biology</i>. 2014;10(1). doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1003408\">10.1371/journal.pcbi.1003408</a>","apa":"Tkačik, G., Marre, O., Amodei, D., Schneidman, E., Bialek, W., &#38; Berry, M. (2014). Searching for collective behavior in a large network of sensory neurons. <i>PLoS Computational Biology</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1003408\">https://doi.org/10.1371/journal.pcbi.1003408</a>","short":"G. Tkačik, O. Marre, D. Amodei, E. Schneidman, W. Bialek, M. Berry, PLoS Computational Biology 10 (2014).","chicago":"Tkačik, Gašper, Olivier Marre, Dario Amodei, Elad Schneidman, William Bialek, and Michael Berry. “Searching for Collective Behavior in a Large Network of Sensory Neurons.” <i>PLoS Computational Biology</i>. Public Library of Science, 2014. <a href=\"https://doi.org/10.1371/journal.pcbi.1003408\">https://doi.org/10.1371/journal.pcbi.1003408</a>.","ista":"Tkačik G, Marre O, Amodei D, Schneidman E, Bialek W, Berry M. 2014. Searching for collective behavior in a large network of sensory neurons. PLoS Computational Biology. 10(1), e1003408.","mla":"Tkačik, Gašper, et al. “Searching for Collective Behavior in a Large Network of Sensory Neurons.” <i>PLoS Computational Biology</i>, vol. 10, no. 1, e1003408, Public Library of Science, 2014, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1003408\">10.1371/journal.pcbi.1003408</a>.","ieee":"G. Tkačik, O. Marre, D. Amodei, E. Schneidman, W. Bialek, and M. Berry, “Searching for collective behavior in a large network of sensory neurons,” <i>PLoS Computational Biology</i>, vol. 10, no. 1. Public Library of Science, 2014."},"article_number":"e1003408","scopus_import":"1","doi":"10.1371/journal.pcbi.1003408","month":"01","pubrep_id":"436","file_date_updated":"2020-07-14T12:45:35Z","has_accepted_license":"1","corr_author":"1","date_published":"2014-01-02T00:00:00Z","volume":10,"language":[{"iso":"eng"}]},{"publication_status":"published","intvolume":"        89","publist_id":"4798","issue":"6","date_created":"2018-12-11T11:56:11Z","day":"16","main_file_link":[{"open_access":"1","url":"http://arxiv.org/abs/1403.3209"}],"user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","isi":1,"article_processing_charge":"No","_id":"2183","acknowledgement":"V.B.S. is partially supported by contract MEC (Grant No. AYA2010-22111-C03-02).\r\n","quality_controlled":"1","external_id":{"arxiv":["1403.3209"],"isi":["000337733900007"]},"status":"public","publication":"Physical Review E Statistical Nonlinear and Soft Matter Physics","department":[{"_id":"GaTk"}],"publisher":"American Institute of Physics","oa":1,"year":"2014","title":"Hierarchy and polysynchrony in an adaptive network ","arxiv":1,"type":"journal_article","date_updated":"2025-09-29T11:34:45Z","citation":{"apa":"Botella Soler, V., &#38; Glendinning, P. (2014). Hierarchy and polysynchrony in an adaptive network . <i>Physical Review E Statistical Nonlinear and Soft Matter Physics</i>. American Institute of Physics. <a href=\"https://doi.org/10.1103/PhysRevE.89.062809\">https://doi.org/10.1103/PhysRevE.89.062809</a>","ama":"Botella Soler V, Glendinning P. Hierarchy and polysynchrony in an adaptive network . <i>Physical Review E Statistical Nonlinear and Soft Matter Physics</i>. 2014;89(6). doi:<a href=\"https://doi.org/10.1103/PhysRevE.89.062809\">10.1103/PhysRevE.89.062809</a>","ieee":"V. Botella Soler and P. Glendinning, “Hierarchy and polysynchrony in an adaptive network ,” <i>Physical Review E Statistical Nonlinear and Soft Matter Physics</i>, vol. 89, no. 6. American Institute of Physics, 2014.","mla":"Botella Soler, Vicente, and Paul Glendinning. “Hierarchy and Polysynchrony in an Adaptive Network .” <i>Physical Review E Statistical Nonlinear and Soft Matter Physics</i>, vol. 89, no. 6, 062809, American Institute of Physics, 2014, doi:<a href=\"https://doi.org/10.1103/PhysRevE.89.062809\">10.1103/PhysRevE.89.062809</a>.","ista":"Botella Soler V, Glendinning P. 2014. Hierarchy and polysynchrony in an adaptive network . Physical Review E Statistical Nonlinear and Soft Matter Physics. 89(6), 062809.","chicago":"Botella Soler, Vicente, and Paul Glendinning. “Hierarchy and Polysynchrony in an Adaptive Network .” <i>Physical Review E Statistical Nonlinear and Soft Matter Physics</i>. American Institute of Physics, 2014. <a href=\"https://doi.org/10.1103/PhysRevE.89.062809\">https://doi.org/10.1103/PhysRevE.89.062809</a>.","short":"V. Botella Soler, P. Glendinning, Physical Review E Statistical Nonlinear and Soft Matter Physics 89 (2014)."},"article_number":"062809","abstract":[{"text":"We describe a simple adaptive network of coupled chaotic maps. The network reaches a stationary state (frozen topology) for all values of the coupling parameter, although the dynamics of the maps at the nodes of the network can be nontrivial. The structure of the network shows interesting hierarchical properties and in certain parameter regions the dynamics is polysynchronous: Nodes can be divided in differently synchronized classes but, contrary to cluster synchronization, nodes in the same class need not be connected to each other. These complicated synchrony patterns have been conjectured to play roles in systems biology and circuits. The adaptive system we study describes ways whereby this behavior can evolve from undifferentiated nodes.","lang":"eng"}],"author":[{"id":"421234E8-F248-11E8-B48F-1D18A9856A87","full_name":"Botella Soler, Vicente","last_name":"Botella Soler","first_name":"Vicente","orcid":"0000-0002-8790-1914"},{"full_name":"Glendinning, Paul","first_name":"Paul","last_name":"Glendinning"}],"oa_version":"Preprint","doi":"10.1103/PhysRevE.89.062809","month":"06","scopus_import":"1","language":[{"iso":"eng"}],"corr_author":"1","project":[{"call_identifier":"FP7","name":"International IST Postdoc Fellowship Programme","_id":"25681D80-B435-11E9-9278-68D0E5697425","grant_number":"291734"}],"ec_funded":1,"volume":89,"date_published":"2014-06-16T00:00:00Z"},{"title":"Spatio-temporal representations of uncertainty in spiking neural networks","year":"2014","date_updated":"2025-06-03T11:45:08Z","type":"conference","citation":{"ama":"Savin C, Denève S. Spatio-temporal representations of uncertainty in spiking neural networks. In: Vol 27. Neural Information Processing Systems Foundation; 2014:2024-2032.","apa":"Savin, C., &#38; Denève, S. (2014). Spatio-temporal representations of uncertainty in spiking neural networks (Vol. 27, pp. 2024–2032). Presented at the NIPS: Neural Information Processing Systems, Montreal, Canada: Neural Information Processing Systems Foundation.","short":"C. Savin, S. Denève, in:, Neural Information Processing Systems Foundation, 2014, pp. 2024–2032.","mla":"Savin, Cristina, and Sophie Denève. <i>Spatio-Temporal Representations of Uncertainty in Spiking Neural Networks</i>. Vol. 27, no. January, Neural Information Processing Systems Foundation, 2014, pp. 2024–32.","ieee":"C. Savin and S. Denève, “Spatio-temporal representations of uncertainty in spiking neural networks,” presented at the NIPS: Neural Information Processing Systems, Montreal, Canada, 2014, vol. 27, no. January, pp. 2024–2032.","ista":"Savin C, Denève S. 2014. Spatio-temporal representations of uncertainty in spiking neural networks. NIPS: Neural Information Processing Systems vol. 27, 2024–2032.","chicago":"Savin, Cristina, and Sophie Denève. “Spatio-Temporal Representations of Uncertainty in Spiking Neural Networks,” 27:2024–32. Neural Information Processing Systems Foundation, 2014."},"abstract":[{"lang":"eng","text":"It has been long argued that, because of inherent ambiguity and noise, the brain needs to represent uncertainty in the form of probability distributions. The neural encoding of such distributions remains however highly controversial. Here we present a novel circuit model for representing multidimensional real-valued distributions using a spike based spatio-temporal code. Our model combines the computational advantages of the currently competing models for probabilistic codes and exhibits realistic neural responses along a variety of classic measures. Furthermore, the model highlights the challenges associated with interpreting neural activity in relation to behavioral uncertainty and points to alternative population-level approaches for the experimental validation of distributed representations."}],"oa_version":"None","conference":{"start_date":"2014-12-08","location":"Montreal, Canada","end_date":"2014-12-13","name":"NIPS: Neural Information Processing Systems"},"author":[{"first_name":"Cristina","last_name":"Savin","full_name":"Savin, Cristina","id":"3933349E-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Denève, Sophie","last_name":"Denève","first_name":"Sophie"}],"month":"01","scopus_import":"1","language":[{"iso":"eng"}],"volume":27,"date_published":"2014-01-01T00:00:00Z","corr_author":"1","intvolume":"        27","publication_status":"published","publist_id":"5427","date_created":"2018-12-11T11:53:35Z","day":"01","issue":"January","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","main_file_link":[{"url":"http://papers.nips.cc/paper/5343-spatio-temporal-representations-of-uncertainty-in-spiking-neural-networks.pdf","open_access":"1"}],"_id":"1708","article_processing_charge":"No","quality_controlled":"1","status":"public","oa":1,"page":"2024 - 2032","department":[{"_id":"GaTk"}],"publisher":"Neural Information Processing Systems Foundation"},{"department":[{"_id":"GaTk"}],"publisher":"eLife Sciences Publications","file":[{"checksum":"766ac8999ac6e3364f10065a06024b8f","date_created":"2018-12-12T10:12:04Z","file_size":5117086,"file_name":"IST-2016-420-v1+1_e03722.full.pdf","relation":"main_file","file_id":"4922","creator":"system","date_updated":"2020-07-14T12:45:20Z","content_type":"application/pdf","access_level":"open_access"}],"oa":1,"status":"public","publication":"eLife","quality_controlled":"1","external_id":{"isi":["000209685300001"]},"ddc":["570"],"isi":1,"_id":"1886","article_processing_charge":"No","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png"},"issue":"November","day":"14","date_created":"2018-12-11T11:54:32Z","publist_id":"5209","publication_status":"published","has_accepted_license":"1","project":[{"name":"Sensitivity to higher-order statistics in natural scenes","_id":"254D1A94-B435-11E9-9278-68D0E5697425","grant_number":"P 25651-N26","call_identifier":"FWF"}],"file_date_updated":"2020-07-14T12:45:20Z","date_published":"2014-11-14T00:00:00Z","language":[{"iso":"eng"}],"scopus_import":"1","pubrep_id":"420","doi":"10.7554/eLife.03722","month":"11","author":[{"first_name":"Ann","last_name":"Hermundstad","full_name":"Hermundstad, Ann"},{"last_name":"Briguglio","first_name":"John","full_name":"Briguglio, John"},{"full_name":"Conte, Mary","last_name":"Conte","first_name":"Mary"},{"full_name":"Victor, Jonathan","first_name":"Jonathan","last_name":"Victor"},{"first_name":"Vijay","last_name":"Balasubramanian","full_name":"Balasubramanian, Vijay"},{"id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","full_name":"Tkacik, Gasper","last_name":"Tkacik","orcid":"0000-0002-6699-1455","first_name":"Gasper"}],"oa_version":"Published Version","citation":{"chicago":"Hermundstad, Ann, John Briguglio, Mary Conte, Jonathan Victor, Vijay Balasubramanian, and Gašper Tkačik. “Variance Predicts Salience in Central Sensory Processing.” <i>ELife</i>. eLife Sciences Publications, 2014. <a href=\"https://doi.org/10.7554/eLife.03722\">https://doi.org/10.7554/eLife.03722</a>.","ista":"Hermundstad A, Briguglio J, Conte M, Victor J, Balasubramanian V, Tkačik G. 2014. Variance predicts salience in central sensory processing. eLife. (November), e03722.","ieee":"A. Hermundstad, J. Briguglio, M. Conte, J. Victor, V. Balasubramanian, and G. Tkačik, “Variance predicts salience in central sensory processing,” <i>eLife</i>, no. November. eLife Sciences Publications, 2014.","mla":"Hermundstad, Ann, et al. “Variance Predicts Salience in Central Sensory Processing.” <i>ELife</i>, no. November, e03722, eLife Sciences Publications, 2014, doi:<a href=\"https://doi.org/10.7554/eLife.03722\">10.7554/eLife.03722</a>.","short":"A. Hermundstad, J. Briguglio, M. Conte, J. Victor, V. Balasubramanian, G. Tkačik, ELife (2014).","apa":"Hermundstad, A., Briguglio, J., Conte, M., Victor, J., Balasubramanian, V., &#38; Tkačik, G. (2014). Variance predicts salience in central sensory processing. <i>ELife</i>. eLife Sciences Publications. <a href=\"https://doi.org/10.7554/eLife.03722\">https://doi.org/10.7554/eLife.03722</a>","ama":"Hermundstad A, Briguglio J, Conte M, Victor J, Balasubramanian V, Tkačik G. Variance predicts salience in central sensory processing. <i>eLife</i>. 2014;(November). doi:<a href=\"https://doi.org/10.7554/eLife.03722\">10.7554/eLife.03722</a>"},"article_number":"e03722","abstract":[{"lang":"eng","text":"Information processing in the sensory periphery is shaped by natural stimulus statistics. In the periphery, a transmission bottleneck constrains performance; thus efficient coding implies that natural signal components with a predictably wider range should be compressed. In a different regime—when sampling limitations constrain performance—efficient coding implies that more resources should be allocated to informative features that are more variable. We propose that this regime is relevant for sensory cortex when it extracts complex features from limited numbers of sensory samples. To test this prediction, we use central visual processing as a model: we show that visual sensitivity for local multi-point spatial correlations, described by dozens of independently-measured parameters, can be quantitatively predicted from the structure of natural images. This suggests that efficient coding applies centrally, where it extends to higher-order sensory features and operates in a regime in which sensitivity increases with feature variability."}],"type":"journal_article","date_updated":"2025-09-29T13:08:33Z","year":"2014","title":"Variance predicts salience in central sensory processing"},{"department":[{"_id":"NiBa"},{"_id":"GaTk"}],"publisher":"American Institute of Physics","oa":1,"status":"public","publication":"Physical Review E Statistical Nonlinear and Soft Matter Physics","external_id":{"isi":["000332274100002"],"arxiv":["1402.0430"]},"isi":1,"article_processing_charge":"No","_id":"1896","acknowledgement":"The work was supported by the VEGA Grant No. 1/0459/13 (R.K. and K.B.).","main_file_link":[{"url":"http://arxiv.org/abs/1402.0430","open_access":"1"}],"user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","issue":"3","date_created":"2018-12-11T11:54:35Z","day":"04","publist_id":"5198","publication_status":"published","intvolume":"        89","volume":89,"date_published":"2014-03-04T00:00:00Z","language":[{"iso":"eng"}],"scopus_import":"1","month":"03","doi":"10.1103/PhysRevE.89.032701","author":[{"full_name":"Kollár, Richard","last_name":"Kollár","first_name":"Richard"},{"full_name":"Bod'ová, Katarína","id":"2BA24EA0-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-7214-0171","first_name":"Katarína","last_name":"Bod'ová"},{"last_name":"Nosek","first_name":"Jozef","full_name":"Nosek, Jozef"},{"full_name":"Tomáška, Ľubomír","first_name":"Ľubomír","last_name":"Tomáška"}],"oa_version":"Submitted Version","article_number":"032701","citation":{"short":"R. Kollár, K. Bodova, J. Nosek, Ľ. Tomáška, Physical Review E Statistical Nonlinear and Soft Matter Physics 89 (2014).","ista":"Kollár R, Bodova K, Nosek J, Tomáška Ľ. 2014. Mathematical model of alternative mechanism of telomere length maintenance. Physical Review E Statistical Nonlinear and Soft Matter Physics. 89(3), 032701.","chicago":"Kollár, Richard, Katarina Bodova, Jozef Nosek, and Ľubomír Tomáška. “Mathematical Model of Alternative Mechanism of Telomere Length Maintenance.” <i>Physical Review E Statistical Nonlinear and Soft Matter Physics</i>. American Institute of Physics, 2014. <a href=\"https://doi.org/10.1103/PhysRevE.89.032701\">https://doi.org/10.1103/PhysRevE.89.032701</a>.","mla":"Kollár, Richard, et al. “Mathematical Model of Alternative Mechanism of Telomere Length Maintenance.” <i>Physical Review E Statistical Nonlinear and Soft Matter Physics</i>, vol. 89, no. 3, 032701, American Institute of Physics, 2014, doi:<a href=\"https://doi.org/10.1103/PhysRevE.89.032701\">10.1103/PhysRevE.89.032701</a>.","ieee":"R. Kollár, K. Bodova, J. Nosek, and Ľ. Tomáška, “Mathematical model of alternative mechanism of telomere length maintenance,” <i>Physical Review E Statistical Nonlinear and Soft Matter Physics</i>, vol. 89, no. 3. American Institute of Physics, 2014.","ama":"Kollár R, Bodova K, Nosek J, Tomáška Ľ. Mathematical model of alternative mechanism of telomere length maintenance. <i>Physical Review E Statistical Nonlinear and Soft Matter Physics</i>. 2014;89(3). doi:<a href=\"https://doi.org/10.1103/PhysRevE.89.032701\">10.1103/PhysRevE.89.032701</a>","apa":"Kollár, R., Bodova, K., Nosek, J., &#38; Tomáška, Ľ. (2014). Mathematical model of alternative mechanism of telomere length maintenance. <i>Physical Review E Statistical Nonlinear and Soft Matter Physics</i>. American Institute of Physics. <a href=\"https://doi.org/10.1103/PhysRevE.89.032701\">https://doi.org/10.1103/PhysRevE.89.032701</a>"},"abstract":[{"text":"Biopolymer length regulation is a complex process that involves a large number of biological, chemical, and physical subprocesses acting simultaneously across multiple spatial and temporal scales. An illustrative example important for genomic stability is the length regulation of telomeres - nucleoprotein structures at the ends of linear chromosomes consisting of tandemly repeated DNA sequences and a specialized set of proteins. Maintenance of telomeres is often facilitated by the enzyme telomerase but, particularly in telomerase-free systems, the maintenance of chromosomal termini depends on alternative lengthening of telomeres (ALT) mechanisms mediated by recombination. Various linear and circular DNA structures were identified to participate in ALT, however, dynamics of the whole process is still poorly understood. We propose a chemical kinetics model of ALT with kinetic rates systematically derived from the biophysics of DNA diffusion and looping. The reaction system is reduced to a coagulation-fragmentation system by quasi-steady-state approximation. The detailed treatment of kinetic rates yields explicit formulas for expected size distributions of telomeres that demonstrate the key role played by the J factor, a quantitative measure of bending of polymers. The results are in agreement with experimental data and point out interesting phenomena: an appearance of very long telomeric circles if the total telomere density exceeds a critical value (excess mass) and a nonlinear response of the telomere size distributions to the amount of telomeric DNA in the system. The results can be of general importance for understanding dynamics of telomeres in telomerase-independent systems as this mode of telomere maintenance is similar to the situation in tumor cells lacking telomerase activity. Furthermore, due to its universality, the model may also serve as a prototype of an interaction between linear and circular DNA structures in various settings.","lang":"eng"}],"arxiv":1,"type":"journal_article","date_updated":"2025-09-29T13:03:34Z","year":"2014","title":"Mathematical model of alternative mechanism of telomere length maintenance"},{"issue":"3","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png"},"day":"01","date_created":"2018-12-11T11:54:40Z","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","publication_status":"published","intvolume":"        28","publist_id":"5186","status":"public","publication":"Functional Ecology","page":"693 - 701","department":[{"_id":"NiBa"},{"_id":"GaTk"}],"publisher":"Wiley-Blackwell","file":[{"access_level":"open_access","content_type":"application/pdf","date_updated":"2020-07-14T12:45:20Z","relation":"main_file","file_id":"5167","creator":"system","file_name":"IST-2016-419-v1+1_Ezard_et_al-2014-Functional_Ecology.pdf","checksum":"3cbe8623174709a8ceec2103246f8fe0","file_size":536154,"date_created":"2018-12-12T10:15:45Z"}],"oa":1,"isi":1,"_id":"1909","article_processing_charge":"No","acknowledgement":"Engineering and Physical Sciences Research Council. Grant Number: EP/H031928/1","external_id":{"isi":["000335954900016"]},"ddc":["570"],"citation":{"short":"T. Ezard, R. Prizak, R. Hoyle, Functional Ecology 28 (2014) 693–701.","ieee":"T. Ezard, R. Prizak, and R. Hoyle, “The fitness costs of adaptation via phenotypic plasticity and maternal effects,” <i>Functional Ecology</i>, vol. 28, no. 3. Wiley-Blackwell, pp. 693–701, 2014.","mla":"Ezard, Thomas, et al. “The Fitness Costs of Adaptation via Phenotypic Plasticity and Maternal Effects.” <i>Functional Ecology</i>, vol. 28, no. 3, Wiley-Blackwell, 2014, pp. 693–701, doi:<a href=\"https://doi.org/10.1111/1365-2435.12207\">10.1111/1365-2435.12207</a>.","chicago":"Ezard, Thomas, Roshan Prizak, and Rebecca Hoyle. “The Fitness Costs of Adaptation via Phenotypic Plasticity and Maternal Effects.” <i>Functional Ecology</i>. Wiley-Blackwell, 2014. <a href=\"https://doi.org/10.1111/1365-2435.12207\">https://doi.org/10.1111/1365-2435.12207</a>.","ista":"Ezard T, Prizak R, Hoyle R. 2014. The fitness costs of adaptation via phenotypic plasticity and maternal effects. Functional Ecology. 28(3), 693–701.","ama":"Ezard T, Prizak R, Hoyle R. The fitness costs of adaptation via phenotypic plasticity and maternal effects. <i>Functional Ecology</i>. 2014;28(3):693-701. doi:<a href=\"https://doi.org/10.1111/1365-2435.12207\">10.1111/1365-2435.12207</a>","apa":"Ezard, T., Prizak, R., &#38; Hoyle, R. (2014). The fitness costs of adaptation via phenotypic plasticity and maternal effects. <i>Functional Ecology</i>. Wiley-Blackwell. <a href=\"https://doi.org/10.1111/1365-2435.12207\">https://doi.org/10.1111/1365-2435.12207</a>"},"abstract":[{"lang":"eng","text":"Summary: Phenotypes are often environmentally dependent, which requires organisms to track environmental change. The challenge for organisms is to construct phenotypes using the most accurate environmental cue. Here, we use a quantitative genetic model of adaptation by additive genetic variance, within- and transgenerational plasticity via linear reaction norms and indirect genetic effects respectively. We show how the relative influence on the eventual phenotype of these components depends on the predictability of environmental change (fast or slow, sinusoidal or stochastic) and the developmental lag τ between when the environment is perceived and when selection acts. We then decompose expected mean fitness into three components (variance load, adaptation and fluctuation load) to study the fitness costs of within- and transgenerational plasticity. A strongly negative maternal effect coefficient m minimizes the variance load, but a strongly positive m minimises the fluctuation load. The adaptation term is maximized closer to zero, with positive or negative m preferred under different environmental scenarios. Phenotypic plasticity is higher when τ is shorter and when the environment changes frequently between seasonal extremes. Expected mean population fitness is highest away from highest observed levels of phenotypic plasticity. Within- and transgenerational plasticity act in concert to deliver well-adapted phenotypes, which emphasizes the need to study both simultaneously when investigating phenotypic evolution."}],"author":[{"first_name":"Thomas","last_name":"Ezard","full_name":"Ezard, Thomas"},{"first_name":"Roshan","last_name":"Prizak","full_name":"Prizak, Roshan","id":"4456104E-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Rebecca","last_name":"Hoyle","full_name":"Hoyle, Rebecca"}],"oa_version":"Published Version","year":"2014","title":"The fitness costs of adaptation via phenotypic plasticity and maternal effects","type":"journal_article","date_updated":"2025-09-29T12:26:34Z","language":[{"iso":"eng"}],"has_accepted_license":"1","file_date_updated":"2020-07-14T12:45:20Z","volume":28,"date_published":"2014-06-01T00:00:00Z","month":"06","pubrep_id":"419","doi":"10.1111/1365-2435.12207","scopus_import":"1"},{"doi":"10.1016/j.jtbi.2014.06.039","month":"11","scopus_import":"1","language":[{"iso":"eng"}],"corr_author":"1","date_published":"2014-11-07T00:00:00Z","volume":360,"year":"2014","title":"Evolutionary dynamics of infectious diseases in finite populations","type":"journal_article","date_updated":"2025-09-29T12:12:23Z","citation":{"apa":"Humplik, J., Hill, A., &#38; Nowak, M. (2014). Evolutionary dynamics of infectious diseases in finite populations. <i>Journal of Theoretical Biology</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.jtbi.2014.06.039\">https://doi.org/10.1016/j.jtbi.2014.06.039</a>","ama":"Humplik J, Hill A, Nowak M. Evolutionary dynamics of infectious diseases in finite populations. <i>Journal of Theoretical Biology</i>. 2014;360:149-162. doi:<a href=\"https://doi.org/10.1016/j.jtbi.2014.06.039\">10.1016/j.jtbi.2014.06.039</a>","ista":"Humplik J, Hill A, Nowak M. 2014. Evolutionary dynamics of infectious diseases in finite populations. Journal of Theoretical Biology. 360, 149–162.","chicago":"Humplik, Jan, Alison Hill, and Martin Nowak. “Evolutionary Dynamics of Infectious Diseases in Finite Populations.” <i>Journal of Theoretical Biology</i>. Elsevier, 2014. <a href=\"https://doi.org/10.1016/j.jtbi.2014.06.039\">https://doi.org/10.1016/j.jtbi.2014.06.039</a>.","ieee":"J. Humplik, A. Hill, and M. Nowak, “Evolutionary dynamics of infectious diseases in finite populations,” <i>Journal of Theoretical Biology</i>, vol. 360. Elsevier, pp. 149–162, 2014.","mla":"Humplik, Jan, et al. “Evolutionary Dynamics of Infectious Diseases in Finite Populations.” <i>Journal of Theoretical Biology</i>, vol. 360, Elsevier, 2014, pp. 149–62, doi:<a href=\"https://doi.org/10.1016/j.jtbi.2014.06.039\">10.1016/j.jtbi.2014.06.039</a>.","short":"J. Humplik, A. Hill, M. Nowak, Journal of Theoretical Biology 360 (2014) 149–162."},"abstract":[{"lang":"eng","text":"In infectious disease epidemiology the basic reproductive ratio, R0, is defined as the average number of new infections caused by a single infected individual in a fully susceptible population. Many models describing competition for hosts between non-interacting pathogen strains in an infinite population lead to the conclusion that selection favors invasion of new strains if and only if they have higher R0 values than the resident. Here we demonstrate that this picture fails in finite populations. Using a simple stochastic SIS model, we show that in general there is no analogous optimization principle. We find that successive invasions may in some cases lead to strains that infect a smaller fraction of the host population, and that mutually invasible pathogen strains exist. In the limit of weak selection we demonstrate that an optimization principle does exist, although it differs from R0 maximization. For strains with very large R0, we derive an expression for this local fitness function and use it to establish a lower bound for the error caused by neglecting stochastic effects. Furthermore, we apply this weak selection limit to investigate the selection dynamics in the presence of a trade-off between the virulence and the transmission rate of a pathogen."}],"author":[{"id":"2E9627A8-F248-11E8-B48F-1D18A9856A87","full_name":"Humplik, Jan","last_name":"Humplik","first_name":"Jan"},{"first_name":"Alison","last_name":"Hill","full_name":"Hill, Alison"},{"full_name":"Nowak, Martin","first_name":"Martin","last_name":"Nowak"}],"oa_version":"None","isi":1,"_id":"1928","article_processing_charge":"No","acknowledgement":"J.H. received support from the Zdenek Bakala Foundation and the Mobility Fund of Charles University in Prague.","external_id":{"isi":["000341800600017"]},"status":"public","publication":"Journal of Theoretical Biology","department":[{"_id":"GaTk"}],"page":"149 - 162","publisher":"Elsevier","publication_status":"published","intvolume":"       360","publist_id":"5166","date_created":"2018-12-11T11:54:46Z","day":"07","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345"},{"year":"2014","title":"Emergence of task-dependent representations in working memory circuits","type":"journal_article","date_updated":"2025-09-29T12:11:13Z","abstract":[{"lang":"eng","text":"A wealth of experimental evidence suggests that working memory circuits preferentially represent information that is behaviorally relevant. Still, we are missing a mechanistic account of how these representations come about. Here we provide a simple explanation for a range of experimental findings, in light of prefrontal circuits adapting to task constraints by reward-dependent learning. In particular, we model a neural network shaped by reward-modulated spike-timing dependent plasticity (r-STDP) and homeostatic plasticity (intrinsic excitability and synaptic scaling). We show that the experimentally-observed neural representations naturally emerge in an initially unstructured circuit as it learns to solve several working memory tasks. These results point to a critical, and previously unappreciated, role for reward-dependent learning in shaping prefrontal cortex activity."}],"article_number":"57","citation":{"short":"C. Savin, J. Triesch, Frontiers in Computational Neuroscience 8 (2014).","ista":"Savin C, Triesch J. 2014. Emergence of task-dependent representations in working memory circuits. Frontiers in Computational Neuroscience. 8(MAY), 57.","chicago":"Savin, Cristina, and Jochen Triesch. “Emergence of Task-Dependent Representations in Working Memory Circuits.” <i>Frontiers in Computational Neuroscience</i>. Frontiers Research Foundation, 2014. <a href=\"https://doi.org/10.3389/fncom.2014.00057\">https://doi.org/10.3389/fncom.2014.00057</a>.","mla":"Savin, Cristina, and Jochen Triesch. “Emergence of Task-Dependent Representations in Working Memory Circuits.” <i>Frontiers in Computational Neuroscience</i>, vol. 8, no. MAY, 57, Frontiers Research Foundation, 2014, doi:<a href=\"https://doi.org/10.3389/fncom.2014.00057\">10.3389/fncom.2014.00057</a>.","ieee":"C. Savin and J. Triesch, “Emergence of task-dependent representations in working memory circuits,” <i>Frontiers in Computational Neuroscience</i>, vol. 8, no. MAY. Frontiers Research Foundation, 2014.","ama":"Savin C, Triesch J. Emergence of task-dependent representations in working memory circuits. <i>Frontiers in Computational Neuroscience</i>. 2014;8(MAY). doi:<a href=\"https://doi.org/10.3389/fncom.2014.00057\">10.3389/fncom.2014.00057</a>","apa":"Savin, C., &#38; Triesch, J. (2014). Emergence of task-dependent representations in working memory circuits. <i>Frontiers in Computational Neuroscience</i>. Frontiers Research Foundation. <a href=\"https://doi.org/10.3389/fncom.2014.00057\">https://doi.org/10.3389/fncom.2014.00057</a>"},"author":[{"id":"3933349E-F248-11E8-B48F-1D18A9856A87","full_name":"Savin, Cristina","last_name":"Savin","first_name":"Cristina"},{"full_name":"Triesch, Jochen","first_name":"Jochen","last_name":"Triesch"}],"oa_version":"Submitted Version","doi":"10.3389/fncom.2014.00057","month":"05","scopus_import":"1","language":[{"iso":"eng"}],"corr_author":"1","date_published":"2014-05-28T00:00:00Z","volume":8,"publication_status":"published","intvolume":"         8","publist_id":"5163","issue":"MAY","date_created":"2018-12-11T11:54:46Z","day":"28","main_file_link":[{"open_access":"1","url":"http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4035833/"}],"user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","article_processing_charge":"No","_id":"1931","isi":1,"acknowledgement":"Supported in part by EC MEXT project PLICON and the LOEWE-Program “Neuronal Coordination Research Focus Frankfurt” (NeFF). Jochen Triesch was supported by the Quandt foundation.","external_id":{"isi":["000336715400001"]},"quality_controlled":"1","publication":"Frontiers in Computational Neuroscience","status":"public","publisher":"Frontiers Research Foundation","department":[{"_id":"GaTk"}],"oa":1},{"intvolume":"         4","publication_status":"published","publist_id":"7280","day":"19","date_created":"2018-12-11T11:47:02Z","issue":"15","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png"},"user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","article_processing_charge":"No","_id":"537","isi":1,"ddc":["530","571"],"external_id":{"isi":["000340575000015"]},"publication":"Ecology and Evolution","status":"public","oa":1,"file":[{"file_name":"IST-2018-934-v1+1_Prizak_et_al-2014-Ecology_and_Evolution.pdf","checksum":"e32abf75a248e7a11811fd7f60858769","date_created":"2018-12-12T10:11:31Z","file_size":621582,"relation":"main_file","file_id":"4886","creator":"system","access_level":"open_access","date_updated":"2020-07-14T12:46:38Z","content_type":"application/pdf"}],"publisher":"Wiley-Blackwell","department":[{"_id":"NiBa"},{"_id":"GaTk"}],"page":"3139 - 3145","title":"Fitness consequences of maternal and grandmaternal effects","year":"2014","date_updated":"2025-09-29T13:17:53Z","type":"journal_article","abstract":[{"text":"Transgenerational effects are broader than only parental relationships. Despite mounting evidence that multigenerational effects alter phenotypic and life-history traits, our understanding of how they combine to determine fitness is not well developed because of the added complexity necessary to study them. Here, we derive a quantitative genetic model of adaptation to an extraordinary new environment by an additive genetic component, phenotypic plasticity, maternal and grandmaternal effects. We show how, at equilibrium, negative maternal and negative grandmaternal effects maximize expected population mean fitness. We define negative transgenerational effects as those that have a negative effect on trait expression in the subsequent generation, that is, they slow, or potentially reverse, the expected evolutionary dynamic. When maternal effects are positive, negative grandmaternal effects are preferred. As expected under Mendelian inheritance, the grandmaternal effects have a lower impact on fitness than the maternal effects, but this dual inheritance model predicts a more complex relationship between maternal and grandmaternal effects to constrain phenotypic variance and so maximize expected population mean fitness in the offspring.","lang":"eng"}],"citation":{"apa":"Prizak, R., Ezard, T., &#38; Hoyle, R. (2014). Fitness consequences of maternal and grandmaternal effects. <i>Ecology and Evolution</i>. Wiley-Blackwell. <a href=\"https://doi.org/10.1002/ece3.1150\">https://doi.org/10.1002/ece3.1150</a>","ama":"Prizak R, Ezard T, Hoyle R. Fitness consequences of maternal and grandmaternal effects. <i>Ecology and Evolution</i>. 2014;4(15):3139-3145. doi:<a href=\"https://doi.org/10.1002/ece3.1150\">10.1002/ece3.1150</a>","ieee":"R. Prizak, T. Ezard, and R. Hoyle, “Fitness consequences of maternal and grandmaternal effects,” <i>Ecology and Evolution</i>, vol. 4, no. 15. Wiley-Blackwell, pp. 3139–3145, 2014.","mla":"Prizak, Roshan, et al. “Fitness Consequences of Maternal and Grandmaternal Effects.” <i>Ecology and Evolution</i>, vol. 4, no. 15, Wiley-Blackwell, 2014, pp. 3139–45, doi:<a href=\"https://doi.org/10.1002/ece3.1150\">10.1002/ece3.1150</a>.","chicago":"Prizak, Roshan, Thomas Ezard, and Rebecca Hoyle. “Fitness Consequences of Maternal and Grandmaternal Effects.” <i>Ecology and Evolution</i>. Wiley-Blackwell, 2014. <a href=\"https://doi.org/10.1002/ece3.1150\">https://doi.org/10.1002/ece3.1150</a>.","ista":"Prizak R, Ezard T, Hoyle R. 2014. Fitness consequences of maternal and grandmaternal effects. Ecology and Evolution. 4(15), 3139–3145.","short":"R. Prizak, T. Ezard, R. Hoyle, Ecology and Evolution 4 (2014) 3139–3145."},"oa_version":"Published Version","author":[{"last_name":"Prizak","first_name":"Roshan","id":"4456104E-F248-11E8-B48F-1D18A9856A87","full_name":"Prizak, Roshan"},{"full_name":"Ezard, Thomas","last_name":"Ezard","first_name":"Thomas"},{"first_name":"Rebecca","last_name":"Hoyle","full_name":"Hoyle, Rebecca"}],"doi":"10.1002/ece3.1150","pubrep_id":"934","month":"07","scopus_import":"1","language":[{"iso":"eng"}],"volume":4,"date_published":"2014-07-19T00:00:00Z","file_date_updated":"2020-07-14T12:46:38Z","has_accepted_license":"1"},{"doi":"10.5061/dryad.246qg","_id":"9752","month":"11","article_processing_charge":"No","oa":1,"date_published":"2014-11-07T00:00:00Z","department":[{"_id":"GaTk"}],"publisher":"Dryad","status":"public","date_updated":"2025-09-29T14:27:23Z","type":"research_data_reference","title":"Data from: Transformation of stimulus correlations by the retina","related_material":{"record":[{"relation":"used_in_publication","status":"public","id":"2277"}]},"year":"2014","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","oa_version":"Published Version","main_file_link":[{"open_access":"1","url":"https://doi.org/10.5061/dryad.246qg"}],"author":[{"first_name":"Kristina","last_name":"Simmons","full_name":"Simmons, Kristina"},{"last_name":"Prentice","first_name":"Jason","full_name":"Prentice, Jason"},{"last_name":"Tkačik","orcid":"0000-0002-6699-1455","first_name":"Gašper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","full_name":"Tkačik, Gašper"},{"full_name":"Homann, Jan","last_name":"Homann","first_name":"Jan"},{"last_name":"Yee","first_name":"Heather","full_name":"Yee, Heather"},{"last_name":"Palmer","first_name":"Stephanie","full_name":"Palmer, Stephanie"},{"full_name":"Nelson, Philip","first_name":"Philip","last_name":"Nelson"},{"full_name":"Balasubramanian, Vijay","last_name":"Balasubramanian","first_name":"Vijay"}],"day":"07","date_created":"2021-07-30T08:13:52Z","citation":{"short":"K. Simmons, J. Prentice, G. Tkačik, J. Homann, H. Yee, S. Palmer, P. Nelson, V. Balasubramanian, (2014).","ista":"Simmons K, Prentice J, Tkačik G, Homann J, Yee H, Palmer S, Nelson P, Balasubramanian V. 2014. Data from: Transformation of stimulus correlations by the retina, Dryad, <a href=\"https://doi.org/10.5061/dryad.246qg\">10.5061/dryad.246qg</a>.","chicago":"Simmons, Kristina, Jason Prentice, Gašper Tkačik, Jan Homann, Heather Yee, Stephanie Palmer, Philip Nelson, and Vijay Balasubramanian. “Data from: Transformation of Stimulus Correlations by the Retina.” Dryad, 2014. <a href=\"https://doi.org/10.5061/dryad.246qg\">https://doi.org/10.5061/dryad.246qg</a>.","mla":"Simmons, Kristina, et al. <i>Data from: Transformation of Stimulus Correlations by the Retina</i>. Dryad, 2014, doi:<a href=\"https://doi.org/10.5061/dryad.246qg\">10.5061/dryad.246qg</a>.","ieee":"K. Simmons <i>et al.</i>, “Data from: Transformation of stimulus correlations by the retina.” Dryad, 2014.","ama":"Simmons K, Prentice J, Tkačik G, et al. Data from: Transformation of stimulus correlations by the retina. 2014. doi:<a href=\"https://doi.org/10.5061/dryad.246qg\">10.5061/dryad.246qg</a>","apa":"Simmons, K., Prentice, J., Tkačik, G., Homann, J., Yee, H., Palmer, S., … Balasubramanian, V. (2014). Data from: Transformation of stimulus correlations by the retina. Dryad. <a href=\"https://doi.org/10.5061/dryad.246qg\">https://doi.org/10.5061/dryad.246qg</a>"},"abstract":[{"text":"Redundancies and correlations in the responses of sensory neurons may seem to waste neural resources, but they can also carry cues about structured stimuli and may help the brain to correct for response errors. To investigate the effect of stimulus structure on redundancy in retina, we measured simultaneous responses from populations of retinal ganglion cells presented with natural and artificial stimuli that varied greatly in correlation structure; these stimuli and recordings are publicly available online. Responding to spatio-temporally structured stimuli such as natural movies, pairs of ganglion cells were modestly more correlated than in response to white noise checkerboards, but they were much less correlated than predicted by a non-adapting functional model of retinal response. Meanwhile, responding to stimuli with purely spatial correlations, pairs of ganglion cells showed increased correlations consistent with a static, non-adapting receptive field and nonlinearity. We found that in response to spatio-temporally correlated stimuli, ganglion cells had faster temporal kernels and tended to have stronger surrounds. These properties of individual cells, along with gain changes that opposed changes in effective contrast at the ganglion cell input, largely explained the pattern of pairwise correlations across stimuli where receptive field measurements were possible.","lang":"eng"}]}]